Overview

Dataset statistics

Number of variables20
Number of observations1408
Missing cells6194
Missing cells (%)22.0%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory220.1 KiB
Average record size in memory160.1 B

Variable types

Categorical7
Text1
Numeric11
DateTime1

Alerts

Dataset has 2 (0.1%) duplicate rowsDuplicates
bb_bf is highly overall correlated with runs and 4 other fieldsHigh correlation
runs is highly overall correlated with bb_bf and 8 other fieldsHigh correlation
wkts is highly overall correlated with wicketball_prob and 1 other fieldsHigh correlation
wicketball_prob is highly overall correlated with runs and 3 other fieldsHigh correlation
runs_per_ball is highly overall correlated with runs and 4 other fieldsHigh correlation
overs is highly overall correlated with bb_bf and 2 other fieldsHigh correlation
econ is highly overall correlated with runs and 2 other fieldsHigh correlation
4s is highly overall correlated with bb_bf and 6 other fieldsHigh correlation
6s is highly overall correlated with runs and 3 other fieldsHigh correlation
sr is highly overall correlated with runs and 4 other fieldsHigh correlation
mins is highly overall correlated with bb_bf and 4 other fieldsHigh correlation
bat_or_bowl is highly overall correlated with bb_bf and 9 other fieldsHigh correlation
mdns is highly overall correlated with bat_or_bowlHigh correlation
not_out is highly overall correlated with bat_or_bowlHigh correlation
mdns is highly imbalanced (54.5%)Imbalance
wkts has 846 (60.1%) missing valuesMissing
overs has 846 (60.1%) missing valuesMissing
mdns has 846 (60.1%) missing valuesMissing
econ has 846 (60.1%) missing valuesMissing
4s has 562 (39.9%) missing valuesMissing
6s has 562 (39.9%) missing valuesMissing
sr has 562 (39.9%) missing valuesMissing
not_out has 562 (39.9%) missing valuesMissing
mins has 562 (39.9%) missing valuesMissing
runs has 65 (4.6%) zerosZeros
wkts has 201 (14.3%) zerosZeros
wicketball_prob has 330 (23.4%) zerosZeros
runs_per_ball has 65 (4.6%) zerosZeros
4s has 255 (18.1%) zerosZeros
6s has 558 (39.6%) zerosZeros
sr has 65 (4.6%) zerosZeros

Reproduction

Analysis started2023-11-20 14:59:02.007055
Analysis finished2023-11-20 14:59:15.653600
Duration13.65 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

team
Categorical

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
NED
157 
AUS
155 
ENG
151 
NZ
144 
BAN
141 
Other values (5)
660 

Length

Max length3
Median length3
Mean length2.6995739
Min length2

Characters and Unicode

Total characters3801
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAK
2nd rowENG
3rd rowNZ
4th rowNZ
5th rowAFG

Common Values

ValueCountFrequency (%)
NED 157
11.2%
AUS 155
11.0%
ENG 151
10.7%
NZ 144
10.2%
BAN 141
10.0%
SA 141
10.0%
SL 138
9.8%
AFG 129
9.2%
PAK 128
9.1%
IND 124
8.8%

Length

2023-11-20T20:29:15.757320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T20:29:15.924820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ned 157
11.2%
aus 155
11.0%
eng 151
10.7%
nz 144
10.2%
ban 141
10.0%
sa 141
10.0%
sl 138
9.8%
afg 129
9.2%
pak 128
9.1%
ind 124
8.8%

Most occurring characters

ValueCountFrequency (%)
N 717
18.9%
A 694
18.3%
S 434
11.4%
E 308
8.1%
D 281
 
7.4%
G 280
 
7.4%
U 155
 
4.1%
Z 144
 
3.8%
B 141
 
3.7%
L 138
 
3.6%
Other values (4) 509
13.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3801
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 717
18.9%
A 694
18.3%
S 434
11.4%
E 308
8.1%
D 281
 
7.4%
G 280
 
7.4%
U 155
 
4.1%
Z 144
 
3.8%
B 141
 
3.7%
L 138
 
3.6%
Other values (4) 509
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3801
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 717
18.9%
A 694
18.3%
S 434
11.4%
E 308
8.1%
D 281
 
7.4%
G 280
 
7.4%
U 155
 
4.1%
Z 144
 
3.8%
B 141
 
3.7%
L 138
 
3.6%
Other values (4) 509
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 717
18.9%
A 694
18.3%
S 434
11.4%
E 308
8.1%
D 281
 
7.4%
G 280
 
7.4%
U 155
 
4.1%
Z 144
 
3.8%
B 141
 
3.7%
L 138
 
3.6%
Other values (4) 509
13.4%

player
Text

Distinct152
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:16.371374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length16.99929
Min length12

Characters and Unicode

Total characters23935
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.4%

Sample

1st rowShaheen Shah Afridi (PAK)
2nd rowDJ Willey (ENG)
3rd rowMJ Henry (NZ)
4th rowLH Ferguson (NZ)
5th rowNoor Ahmad (AFG)
ValueCountFrequency (%)
sa 166
 
3.8%
ned 156
 
3.5%
aus 155
 
3.5%
eng 151
 
3.4%
nz 144
 
3.3%
ban 141
 
3.2%
sl 138
 
3.1%
afg 129
 
2.9%
pak 128
 
2.9%
ind 124
 
2.8%
Other values (270) 2977
67.5%
2023-11-20T20:29:16.988901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3001
 
12.5%
a 2030
 
8.5%
( 1423
 
5.9%
) 1423
 
5.9%
A 1121
 
4.7%
e 901
 
3.8%
N 836
 
3.5%
S 817
 
3.4%
h 814
 
3.4%
i 750
 
3.1%
Other values (48) 10819
45.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10487
43.8%
Uppercase Letter 7539
31.5%
Space Separator 3001
 
12.5%
Open Punctuation 1423
 
5.9%
Close Punctuation 1423
 
5.9%
Dash Punctuation 38
 
0.2%
Decimal Number 15
 
0.1%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2030
19.4%
e 901
 
8.6%
h 814
 
7.8%
i 750
 
7.2%
n 730
 
7.0%
r 627
 
6.0%
m 571
 
5.4%
l 546
 
5.2%
d 519
 
4.9%
s 501
 
4.8%
Other values (16) 2498
23.8%
Uppercase Letter
ValueCountFrequency (%)
A 1121
14.9%
N 836
 
11.1%
S 817
 
10.8%
M 532
 
7.1%
D 489
 
6.5%
G 374
 
5.0%
E 361
 
4.8%
K 298
 
4.0%
R 288
 
3.8%
P 271
 
3.6%
Other values (15) 2152
28.5%
Decimal Number
ValueCountFrequency (%)
3 9
60.0%
1 6
40.0%
Space Separator
ValueCountFrequency (%)
3001
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1423
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1423
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Other Punctuation
ValueCountFrequency (%)
' 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18026
75.3%
Common 5909
 
24.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2030
 
11.3%
A 1121
 
6.2%
e 901
 
5.0%
N 836
 
4.6%
S 817
 
4.5%
h 814
 
4.5%
i 750
 
4.2%
n 730
 
4.0%
r 627
 
3.5%
m 571
 
3.2%
Other values (41) 8829
49.0%
Common
ValueCountFrequency (%)
3001
50.8%
( 1423
24.1%
) 1423
24.1%
- 38
 
0.6%
' 9
 
0.2%
3 9
 
0.2%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3001
 
12.5%
a 2030
 
8.5%
( 1423
 
5.9%
) 1423
 
5.9%
A 1121
 
4.7%
e 901
 
3.8%
N 836
 
3.5%
S 817
 
3.4%
h 814
 
3.4%
i 750
 
3.1%
Other values (48) 10819
45.2%

bat_or_bowl
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
bat
846 
bowl
562 

Length

Max length4
Median length3
Mean length3.3991477
Min length3

Characters and Unicode

Total characters4786
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbowl
2nd rowbowl
3rd rowbowl
4th rowbowl
5th rowbowl

Common Values

ValueCountFrequency (%)
bat 846
60.1%
bowl 562
39.9%

Length

2023-11-20T20:29:17.132252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T20:29:17.223855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bat 846
60.1%
bowl 562
39.9%

Most occurring characters

ValueCountFrequency (%)
b 1408
29.4%
a 846
17.7%
t 846
17.7%
o 562
 
11.7%
w 562
 
11.7%
l 562
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4786
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 1408
29.4%
a 846
17.7%
t 846
17.7%
o 562
 
11.7%
w 562
 
11.7%
l 562
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4786
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 1408
29.4%
a 846
17.7%
t 846
17.7%
o 562
 
11.7%
w 562
 
11.7%
l 562
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 1408
29.4%
a 846
17.7%
t 846
17.7%
o 562
 
11.7%
w 562
 
11.7%
l 562
 
11.7%

bb_bf
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.305398
Minimum0
Maximum143
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:17.326498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q113.75
median32
Q354
95-th percentile75
Maximum143
Range143
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation25.248709
Coefficient of variation (CV)0.71515151
Kurtosis0.67956216
Mean35.305398
Median Absolute Deviation (MAD)20
Skewness0.77338129
Sum49710
Variance637.49728
MonotonicityNot monotonic
2023-11-20T20:29:17.491112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 170
 
12.1%
54 65
 
4.6%
48 61
 
4.3%
42 56
 
4.0%
24 48
 
3.4%
18 44
 
3.1%
30 42
 
3.0%
12 41
 
2.9%
1 40
 
2.8%
36 39
 
2.8%
Other values (107) 802
57.0%
ValueCountFrequency (%)
0 1
 
0.1%
1 40
2.8%
2 25
1.8%
3 30
2.1%
4 28
2.0%
5 24
1.7%
6 38
2.7%
7 25
1.8%
8 24
1.7%
9 18
1.3%
ValueCountFrequency (%)
143 1
 
0.1%
140 1
 
0.1%
132 1
 
0.1%
128 1
 
0.1%
127 1
 
0.1%
124 1
 
0.1%
121 3
0.2%
119 1
 
0.1%
118 1
 
0.1%
116 3
0.2%

runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.237216
Minimum0
Maximum201
Zeros65
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:17.662779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median29
Q349
95-th percentile85
Maximum201
Range201
Interquartile range (IQR)38

Descriptive statistics

Standard deviation28.056329
Coefficient of variation (CV)0.84412393
Kurtosis2.5872257
Mean33.237216
Median Absolute Deviation (MAD)19
Skewness1.277075
Sum46798
Variance787.15762
MonotonicityNot monotonic
2023-11-20T20:29:17.822368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
4.6%
1 44
 
3.1%
2 36
 
2.6%
4 34
 
2.4%
11 31
 
2.2%
16 28
 
2.0%
12 28
 
2.0%
10 28
 
2.0%
9 28
 
2.0%
5 25
 
1.8%
Other values (118) 1061
75.4%
ValueCountFrequency (%)
0 65
4.6%
1 44
3.1%
2 36
2.6%
3 20
 
1.4%
4 34
2.4%
5 25
 
1.8%
6 22
 
1.6%
7 22
 
1.6%
8 23
 
1.6%
9 28
2.0%
ValueCountFrequency (%)
201 1
0.1%
177 1
0.1%
174 1
0.1%
163 1
0.1%
152 1
0.1%
140 1
0.1%
134 1
0.1%
133 1
0.1%
131 2
0.1%
130 1
0.1%

wkts
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)1.2%
Missing846
Missing (%)60.1%
Infinite0
Infinite (%)0.0%
Mean1.2046263
Minimum0
Maximum7
Zeros201
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:17.948677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1982369
Coefficient of variation (CV)0.99469596
Kurtosis0.89729461
Mean1.2046263
Median Absolute Deviation (MAD)1
Skewness0.96050567
Sum677
Variance1.4357718
MonotonicityNot monotonic
2023-11-20T20:29:18.076857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 201
 
14.3%
1 155
 
11.0%
2 130
 
9.2%
3 51
 
3.6%
4 18
 
1.3%
5 6
 
0.4%
7 1
 
0.1%
(Missing) 846
60.1%
ValueCountFrequency (%)
0 201
14.3%
1 155
11.0%
2 130
9.2%
3 51
 
3.6%
4 18
 
1.3%
5 6
 
0.4%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
5 6
 
0.4%
4 18
 
1.3%
3 51
 
3.6%
2 130
9.2%
1 155
11.0%
0 201
14.3%

wicketball_prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070573564
Minimum0
Maximum1
Zeros330
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:18.218212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.010724091
median0.032258065
Q30.0625
95-th percentile0.25
Maximum1
Range1
Interquartile range (IQR)0.05177591

Descriptive statistics

Standard deviation0.15253478
Coefficient of variation (CV)2.1613587
Kurtosis24.630256
Mean0.070573564
Median Absolute Deviation (MAD)0.026565464
Skewness4.7396005
Sum99.367578
Variance0.02326686
MonotonicityNot monotonic
2023-11-20T20:29:18.375446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 330
23.4%
0.033333333 71
 
5.0%
0.016666667 61
 
4.3%
0.05 36
 
2.6%
0.055555556 35
 
2.5%
0.041666667 34
 
2.4%
0.066666667 32
 
2.3%
0.037037037 32
 
2.3%
0.083333333 28
 
2.0%
1 27
 
1.9%
Other values (119) 722
51.3%
ValueCountFrequency (%)
0 330
23.4%
0.007142857 1
 
0.1%
0.007874016 1
 
0.1%
0.008064516 1
 
0.1%
0.008403361 1
 
0.1%
0.008474576 1
 
0.1%
0.00862069 3
 
0.2%
0.008849558 2
 
0.1%
0.009009009 1
 
0.1%
0.009090909 1
 
0.1%
ValueCountFrequency (%)
1 27
1.9%
0.5 20
1.4%
0.333333333 19
1.3%
0.25 21
1.5%
0.222222222 1
 
0.1%
0.2 21
1.5%
0.166666667 24
1.7%
0.142857143 20
1.4%
0.125 21
1.5%
0.118644068 1
 
0.1%

runs_per_ball
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct645
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89875545
Minimum0
Maximum6
Zeros65
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:18.501791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11597222
Q10.62135
median0.87900517
Q31.1308189
95-th percentile1.6666667
Maximum6
Range6
Interquartile range (IQR)0.50946887

Descriptive statistics

Standard deviation0.47105086
Coefficient of variation (CV)0.52411461
Kurtosis10.545905
Mean0.89875545
Median Absolute Deviation (MAD)0.25415
Skewness1.3884496
Sum1265.4477
Variance0.22188892
MonotonicityNot monotonic
2023-11-20T20:29:18.626177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
4.6%
1 59
 
4.2%
0.5 31
 
2.2%
0.8 21
 
1.5%
0.75 17
 
1.2%
1.166666667 15
 
1.1%
1.5 14
 
1.0%
1.333333333 14
 
1.0%
0.916666667 13
 
0.9%
0.3333 12
 
0.9%
Other values (635) 1147
81.5%
ValueCountFrequency (%)
0 65
4.6%
0.0416 1
 
0.1%
0.05 1
 
0.1%
0.0909 1
 
0.1%
0.1 1
 
0.1%
0.1111 1
 
0.1%
0.111111111 1
 
0.1%
0.125 2
 
0.1%
0.1428 4
 
0.3%
0.1538 1
 
0.1%
ValueCountFrequency (%)
6 1
0.1%
3 2
0.1%
2.666666667 2
0.1%
2.6428 1
0.1%
2.5555 1
0.1%
2.5294 1
0.1%
2.5 1
0.1%
2.409 1
0.1%
2.3333 2
0.1%
2.2666 1
0.1%

opposition
Categorical

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
v India
166 
v South Africa
163 
v Australia
152 
v New Zealand
144 
v Pakistan
139 
Other values (5)
644 

Length

Max length14
Median length12
Mean length11.253551
Min length7

Characters and Unicode

Total characters15845
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowv South Africa
2nd rowv India
3rd rowv England
4th rowv Bangladesh
5th rowv Pakistan

Common Values

ValueCountFrequency (%)
v India 166
11.8%
v South Africa 163
11.6%
v Australia 152
10.8%
v New Zealand 144
10.2%
v Pakistan 139
9.9%
v England 134
9.5%
v Netherlands 134
9.5%
v Afghanistan 130
9.2%
v Bangladesh 123
8.7%
v Sri Lanka 123
8.7%

Length

2023-11-20T20:29:18.766810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T20:29:18.892215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
v 1408
43.4%
india 166
 
5.1%
south 163
 
5.0%
africa 163
 
5.0%
australia 152
 
4.7%
new 144
 
4.4%
zealand 144
 
4.4%
pakistan 139
 
4.3%
england 134
 
4.1%
netherlands 134
 
4.1%
Other values (4) 499
 
15.4%

Most occurring characters

ValueCountFrequency (%)
a 2219
14.0%
1838
 
11.6%
v 1408
 
8.9%
n 1357
 
8.6%
i 873
 
5.5%
t 718
 
4.5%
d 701
 
4.4%
l 687
 
4.3%
e 679
 
4.3%
s 678
 
4.3%
Other values (18) 4687
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12169
76.8%
Space Separator 1838
 
11.6%
Uppercase Letter 1838
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2219
18.2%
v 1408
11.6%
n 1357
11.2%
i 873
 
7.2%
t 718
 
5.9%
d 701
 
5.8%
l 687
 
5.6%
e 679
 
5.6%
s 678
 
5.6%
r 572
 
4.7%
Other values (8) 2277
18.7%
Uppercase Letter
ValueCountFrequency (%)
A 445
24.2%
S 286
15.6%
N 278
15.1%
I 166
 
9.0%
Z 144
 
7.8%
P 139
 
7.6%
E 134
 
7.3%
B 123
 
6.7%
L 123
 
6.7%
Space Separator
ValueCountFrequency (%)
1838
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14007
88.4%
Common 1838
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2219
15.8%
v 1408
 
10.1%
n 1357
 
9.7%
i 873
 
6.2%
t 718
 
5.1%
d 701
 
5.0%
l 687
 
4.9%
e 679
 
4.8%
s 678
 
4.8%
r 572
 
4.1%
Other values (17) 4115
29.4%
Common
ValueCountFrequency (%)
1838
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2219
14.0%
1838
 
11.6%
v 1408
 
8.9%
n 1357
 
8.6%
i 873
 
5.5%
t 718
 
4.5%
d 701
 
4.4%
l 687
 
4.3%
e 679
 
4.3%
s 678
 
4.3%
Other values (18) 4687
29.6%

ground
Categorical

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Dharamsala
156 
Eden Gardens
156 
Delhi
153 
Lucknow
149 
Wankhede
148 
Other values (5)
646 

Length

Max length12
Median length9
Mean length7.9815341
Min length4

Characters and Unicode

Total characters11238
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChennai
2nd rowLucknow
3rd rowAhmedabad
4th rowChennai
5th rowChennai

Common Values

ValueCountFrequency (%)
Dharamsala 156
11.1%
Eden Gardens 156
11.1%
Delhi 153
10.9%
Lucknow 149
10.6%
Wankhede 148
10.5%
Chennai 145
10.3%
Bengaluru 144
10.2%
Pune 142
10.1%
Ahmedabad 117
8.3%
Hyderabad 98
7.0%

Length

2023-11-20T20:29:19.049346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T20:29:19.174936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dharamsala 156
10.0%
eden 156
10.0%
gardens 156
10.0%
delhi 153
9.8%
lucknow 149
9.5%
wankhede 148
9.5%
chennai 145
9.3%
bengaluru 144
9.2%
pune 142
9.1%
ahmedabad 117
7.5%

Most occurring characters

ValueCountFrequency (%)
a 1647
14.7%
e 1407
12.5%
n 1185
 
10.5%
d 890
 
7.9%
h 719
 
6.4%
u 579
 
5.2%
r 554
 
4.9%
l 453
 
4.0%
s 312
 
2.8%
D 309
 
2.7%
Other values (19) 3183
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9518
84.7%
Uppercase Letter 1564
 
13.9%
Space Separator 156
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1647
17.3%
e 1407
14.8%
n 1185
12.5%
d 890
9.4%
h 719
7.6%
u 579
 
6.1%
r 554
 
5.8%
l 453
 
4.8%
s 312
 
3.3%
i 298
 
3.1%
Other values (8) 1474
15.5%
Uppercase Letter
ValueCountFrequency (%)
D 309
19.8%
G 156
10.0%
E 156
10.0%
L 149
9.5%
W 148
9.5%
C 145
9.3%
B 144
9.2%
P 142
9.1%
A 117
 
7.5%
H 98
 
6.3%
Space Separator
ValueCountFrequency (%)
156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11082
98.6%
Common 156
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1647
14.9%
e 1407
12.7%
n 1185
 
10.7%
d 890
 
8.0%
h 719
 
6.5%
u 579
 
5.2%
r 554
 
5.0%
l 453
 
4.1%
s 312
 
2.8%
D 309
 
2.8%
Other values (18) 3027
27.3%
Common
ValueCountFrequency (%)
156
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1647
14.7%
e 1407
12.5%
n 1185
 
10.5%
d 890
 
7.9%
h 719
 
6.4%
u 579
 
5.2%
r 554
 
4.9%
l 453
 
4.0%
s 312
 
2.8%
D 309
 
2.7%
Other values (19) 3183
28.3%
Distinct41
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Minimum2023-10-05 00:00:00
Maximum2023-11-16 00:00:00
2023-11-20T20:29:19.331681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:19.456968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)

overs
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)6.8%
Missing846
Missing (%)60.1%
Infinite0
Infinite (%)0.0%
Mean7.3425267
Minimum0.3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:19.599733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile2
Q15.55
median8
Q310
95-th percentile10
Maximum10
Range9.7
Interquartile range (IQR)4.45

Descriptive statistics

Standard deviation2.6797363
Coefficient of variation (CV)0.36496106
Kurtosis-0.41820524
Mean7.3425267
Median Absolute Deviation (MAD)2
Skewness-0.8153724
Sum4126.5
Variance7.1809868
MonotonicityNot monotonic
2023-11-20T20:29:19.740589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10 168
 
11.9%
9 61
 
4.3%
8 57
 
4.0%
7 49
 
3.5%
6 34
 
2.4%
4 33
 
2.3%
5 32
 
2.3%
3 27
 
1.9%
2 23
 
1.6%
1 12
 
0.9%
Other values (28) 66
 
4.7%
(Missing) 846
60.1%
ValueCountFrequency (%)
0.3 3
 
0.2%
0.4 1
 
0.1%
0.5 1
 
0.1%
1 12
 
0.9%
2 23
1.6%
3 27
1.9%
4 33
2.3%
4.2 1
 
0.1%
4.3 1
 
0.1%
4.4 1
 
0.1%
ValueCountFrequency (%)
10 168
11.9%
9.5 2
 
0.1%
9.4 4
 
0.3%
9.3 6
 
0.4%
9.2 4
 
0.3%
9.1 1
 
0.1%
9 61
 
4.3%
8.5 4
 
0.3%
8.4 1
 
0.1%
8.3 4
 
0.3%

mdns
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.7%
Missing846
Missing (%)60.1%
Memory size11.1 KiB
0.0
440 
1.0
104 
2.0
 
14
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 440
31.2%
1.0 104
 
7.4%
2.0 14
 
1.0%
3.0 4
 
0.3%
(Missing) 846
60.1%

Length

2023-11-20T20:29:19.881981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T20:29:20.007782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 440
78.3%
1.0 104
 
18.5%
2.0 14
 
2.5%
3.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1002
59.4%
. 562
33.3%
1 104
 
6.2%
2 14
 
0.8%
3 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124
66.7%
Other Punctuation 562
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1002
89.1%
1 104
 
9.3%
2 14
 
1.2%
3 4
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1002
59.4%
. 562
33.3%
1 104
 
6.2%
2 14
 
0.8%
3 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1002
59.4%
. 562
33.3%
1 104
 
6.2%
2 14
 
0.8%
3 4
 
0.2%

econ
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct230
Distinct (%)40.9%
Missing846
Missing (%)60.1%
Infinite0
Infinite (%)0.0%
Mean5.946637
Minimum1.35
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:20.149634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.35
5-th percentile2.8
Q14.5
median5.675
Q37.12
95-th percentile9.956
Maximum16
Range14.65
Interquartile range (IQR)2.62

Descriptive statistics

Standard deviation2.1415663
Coefficient of variation (CV)0.36013066
Kurtosis1.6862701
Mean5.946637
Median Absolute Deviation (MAD)1.325
Skewness0.85278729
Sum3342.01
Variance4.5863061
MonotonicityNot monotonic
2023-11-20T20:29:20.306691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 18
 
1.3%
7 15
 
1.1%
8 13
 
0.9%
5.5 13
 
0.9%
4.5 11
 
0.8%
5 10
 
0.7%
4 8
 
0.6%
5.4 7
 
0.5%
3.5 7
 
0.5%
8.5 7
 
0.5%
Other values (220) 453
32.2%
(Missing) 846
60.1%
ValueCountFrequency (%)
1.35 1
0.1%
1.5 2
0.1%
1.6 1
0.1%
2 2
0.1%
2.1 1
0.1%
2.2 1
0.1%
2.25 1
0.1%
2.28 2
0.1%
2.4 1
0.1%
2.5 1
0.1%
ValueCountFrequency (%)
16 2
0.1%
13.5 1
 
0.1%
12.66 1
 
0.1%
12.54 1
 
0.1%
12 2
0.1%
11.75 2
0.1%
11.5 2
0.1%
11.16 1
 
0.1%
11 4
0.3%
10.85 1
 
0.1%

inns
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
1
745 
2
663 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1408
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 745
52.9%
2 663
47.1%

Length

2023-11-20T20:29:20.448452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T20:29:20.542116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 745
52.9%
2 663
47.1%

Most occurring characters

ValueCountFrequency (%)
1 745
52.9%
2 663
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1408
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 745
52.9%
2 663
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 745
52.9%
2 663
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 745
52.9%
2 663
47.1%

4s
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)2.4%
Missing562
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean2.6052009
Minimum0
Maximum21
Zeros255
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:20.636691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1469224
Coefficient of variation (CV)1.2079385
Kurtosis4.1974074
Mean2.6052009
Median Absolute Deviation (MAD)2
Skewness1.8380005
Sum2204
Variance9.9031208
MonotonicityNot monotonic
2023-11-20T20:29:20.731868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 255
18.1%
1 164
 
11.6%
2 116
 
8.2%
3 84
 
6.0%
4 60
 
4.3%
6 37
 
2.6%
5 31
 
2.2%
7 24
 
1.7%
8 22
 
1.6%
9 17
 
1.2%
Other values (10) 36
 
2.6%
(Missing) 562
39.9%
ValueCountFrequency (%)
0 255
18.1%
1 164
11.6%
2 116
8.2%
3 84
 
6.0%
4 60
 
4.3%
5 31
 
2.2%
6 37
 
2.6%
7 24
 
1.7%
8 22
 
1.6%
9 17
 
1.2%
ValueCountFrequency (%)
21 1
 
0.1%
19 1
 
0.1%
17 1
 
0.1%
16 2
 
0.1%
15 2
 
0.1%
14 3
 
0.2%
13 1
 
0.1%
12 5
0.4%
11 8
0.6%
10 12
0.9%

6s
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)1.4%
Missing562
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean0.75177305
Minimum0
Maximum11
Zeros558
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:20.826216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5041844
Coefficient of variation (CV)2.0008491
Kurtosis11.274448
Mean0.75177305
Median Absolute Deviation (MAD)0
Skewness3.0479762
Sum636
Variance2.2625708
MonotonicityNot monotonic
2023-11-20T20:29:20.935884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 558
39.6%
1 149
 
10.6%
2 60
 
4.3%
3 30
 
2.1%
4 18
 
1.3%
5 11
 
0.8%
6 7
 
0.5%
7 4
 
0.3%
8 4
 
0.3%
9 3
 
0.2%
Other values (2) 2
 
0.1%
(Missing) 562
39.9%
ValueCountFrequency (%)
0 558
39.6%
1 149
 
10.6%
2 60
 
4.3%
3 30
 
2.1%
4 18
 
1.3%
5 11
 
0.8%
6 7
 
0.5%
7 4
 
0.3%
8 4
 
0.3%
9 3
 
0.2%
ValueCountFrequency (%)
11 1
 
0.1%
10 1
 
0.1%
9 3
 
0.2%
8 4
 
0.3%
7 4
 
0.3%
6 7
 
0.5%
5 11
 
0.8%
4 18
 
1.3%
3 30
2.1%
2 60
4.3%

sr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct429
Distinct (%)50.7%
Missing562
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean83.716596
Minimum0
Maximum600
Zeros65
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:21.045261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q151.61
median81.81
Q3107.02
95-th percentile171.42
Maximum600
Range600
Interquartile range (IQR)55.41

Descriptive statistics

Standard deviation52.475444
Coefficient of variation (CV)0.62682248
Kurtosis11.543246
Mean83.716596
Median Absolute Deviation (MAD)27.93
Skewness1.6836767
Sum70824.24
Variance2753.6723
MonotonicityNot monotonic
2023-11-20T20:29:21.172477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
4.6%
100 41
 
2.9%
50 25
 
1.8%
80 14
 
1.0%
66.66 13
 
0.9%
33.33 12
 
0.9%
133.33 12
 
0.9%
83.33 12
 
0.9%
25 9
 
0.6%
60 9
 
0.6%
Other values (419) 634
45.0%
(Missing) 562
39.9%
ValueCountFrequency (%)
0 65
4.6%
4.16 1
 
0.1%
5 1
 
0.1%
9.09 1
 
0.1%
10 1
 
0.1%
11.11 2
 
0.1%
12.5 2
 
0.1%
14.28 4
 
0.3%
15.38 1
 
0.1%
16.66 3
 
0.2%
ValueCountFrequency (%)
600 1
0.1%
300 2
0.1%
264.28 1
0.1%
255.55 1
0.1%
252.94 1
0.1%
250 1
0.1%
240.9 1
0.1%
233.33 2
0.1%
226.66 1
0.1%
220 1
0.1%

not_out
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing562
Missing (%)39.9%
Memory size11.1 KiB
0.0
718 
1.0
128 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2538
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 718
51.0%
1.0 128
 
9.1%
(Missing) 562
39.9%

Length

2023-11-20T20:29:21.281062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T20:29:21.376945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 718
84.9%
1.0 128
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1564
61.6%
. 846
33.3%
1 128
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1692
66.7%
Other Punctuation 846
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1564
92.4%
1 128
 
7.6%
Other Punctuation
ValueCountFrequency (%)
. 846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1564
61.6%
. 846
33.3%
1 128
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1564
61.6%
. 846
33.3%
1 128
 
5.0%

mins
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct156
Distinct (%)18.4%
Missing562
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean42.72104
Minimum1
Maximum217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-11-20T20:29:21.485409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median28
Q360
95-th percentile132.75
Maximum217
Range216
Interquartile range (IQR)48

Descriptive statistics

Standard deviation41.576908
Coefficient of variation (CV)0.97321852
Kurtosis1.7971423
Mean42.72104
Median Absolute Deviation (MAD)20
Skewness1.4622465
Sum36142
Variance1728.6392
MonotonicityNot monotonic
2023-11-20T20:29:21.610501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 29
 
2.1%
2 23
 
1.6%
6 21
 
1.5%
7 19
 
1.3%
5 17
 
1.2%
4 17
 
1.2%
9 17
 
1.2%
14 16
 
1.1%
23 16
 
1.1%
21 16
 
1.1%
Other values (146) 655
46.5%
(Missing) 562
39.9%
ValueCountFrequency (%)
1 29
2.1%
2 23
1.6%
3 15
1.1%
4 17
1.2%
5 17
1.2%
6 21
1.5%
7 19
1.3%
8 16
1.1%
9 17
1.2%
10 13
0.9%
ValueCountFrequency (%)
217 1
 
0.1%
195 1
 
0.1%
193 1
 
0.1%
192 2
0.1%
182 1
 
0.1%
181 1
 
0.1%
180 3
0.2%
177 1
 
0.1%
176 1
 
0.1%
173 1
 
0.1%

Interactions

2023-11-20T20:29:13.951188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:02.836897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.049564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.125342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.196556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.281225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.769445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.735830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.758599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.796618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.804458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.100110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:02.936527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.143385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.218750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.296298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.416394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.861055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.847357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.852659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.905996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.899113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.235466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.044229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.245349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.338988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.390480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.534673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.938626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.949545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.950065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.015477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.000298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.315768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.141133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.346141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.454179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.493524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.665803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.040151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.089414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.035623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.098742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.083996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.436018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.265592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.447602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.555182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.585984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.788651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.125680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.172287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.117637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.195006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.167201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.515878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.388490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.546371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.656080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.670742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.898312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.203600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.273802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.216656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.282990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.283583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.588068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.491109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.623078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.757302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.773268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.014138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.310267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.364834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.287509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.350084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.368539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.670092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.611512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.730369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.885207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.867694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.103845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.408866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.451718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.376376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.437750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.449167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.753212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.732097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.831479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.974275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.958464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.205160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.493592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.536173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.471778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.521465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.556997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.841120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.837739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:04.927058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.042871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.058452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.580155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.569723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.607612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.584480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.617909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.702762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:14.933173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:03.957817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:05.024423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:06.106137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:07.155838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:08.676262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:09.663529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:10.672666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:11.690703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:12.704412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T20:29:13.833000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-20T20:29:21.749378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
bb_bfrunswktswicketball_probruns_per_balloversecon4s6ssrminsteambat_or_bowloppositiongroundmdnsinnsnot_out
bb_bf1.0000.9020.473-0.4490.1941.000-0.2060.8090.4960.2960.9730.0270.5590.0350.0000.1020.0970.157
runs0.9021.0000.216-0.5030.5470.6500.5020.8900.6400.6070.9140.0400.4760.0340.0460.0700.0960.133
wkts0.4730.2161.0000.945-0.2730.473-0.273NaNNaNNaNNaN0.0591.0000.0000.0000.1300.0000.000
wicketball_prob-0.449-0.5030.9451.000-0.3780.257-0.241-0.556-0.418-0.423-0.6600.0000.2880.0270.0000.0000.0000.209
runs_per_ball0.1940.547-0.273-0.3781.000-0.2061.0000.5600.5721.0000.3230.0530.2330.0630.1080.2220.0690.262
overs1.0000.6500.4730.257-0.2061.000-0.206NaNNaNNaNNaN0.0411.0000.0000.0890.0610.2090.000
econ-0.2060.502-0.273-0.2411.000-0.2061.000NaNNaNNaNNaN0.0871.0000.1070.0850.2610.0870.000
4s0.8090.890NaN-0.5560.560NaNNaN1.0000.4460.5600.7980.0601.0000.0670.0210.0000.0540.124
6s0.4960.640NaN-0.4180.572NaNNaN0.4461.0000.5720.4980.0771.0000.0500.0430.0000.0000.142
sr0.2960.607NaN-0.4231.000NaNNaN0.5600.5721.0000.3230.0991.0000.0830.0920.0000.0700.267
mins0.9730.914NaN-0.6600.323NaNNaN0.7980.4980.3231.0000.0271.0000.0440.0000.0000.0000.145
team0.0270.0400.0590.0000.0530.0410.0870.0600.0770.0990.0271.0000.0000.1070.2450.0590.0000.073
bat_or_bowl0.5590.4761.0000.2880.2331.0001.0001.0001.0001.0001.0000.0001.0000.0000.0001.0000.0001.000
opposition0.0350.0340.0000.0270.0630.0000.1070.0670.0500.0830.0440.1070.0001.0000.2420.0000.0000.034
ground0.0000.0460.0000.0000.1080.0890.0850.0210.0430.0920.0000.2450.0000.2421.0000.0530.0000.000
mdns0.1020.0700.1300.0000.2220.0610.2610.0000.0000.0000.0000.0591.0000.0000.0531.0000.0000.000
inns0.0970.0960.0000.0000.0690.2090.0870.0540.0000.0700.0000.0000.0000.0000.0000.0001.0000.033
not_out0.1570.1330.0000.2090.2620.0000.0000.1240.1420.2670.1450.0731.0000.0340.0000.0000.0331.000

Missing values

2023-11-20T20:29:15.079109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-20T20:29:15.322053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-20T20:29:15.514630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

teamplayerbat_or_bowlbb_bfrunswktswicketball_probruns_per_balloppositiongroundstart_dateoversmdnseconinns4s6ssrnot_outmins
0PAKShaheen Shah Afridi (PAK)bowl60453.00.0500000.750000v South AfricaChennai27-Oct-2310.00.04.502NaNNaNNaNNaNNaN
1ENGDJ Willey (ENG)bowl60453.00.0500000.750000v IndiaLucknow29-Oct-2310.02.04.501NaNNaNNaNNaNNaN
2NZMJ Henry (NZ)bowl60483.00.0500000.800000v EnglandAhmedabad5-Oct-2310.01.04.801NaNNaNNaNNaNNaN
3NZLH Ferguson (NZ)bowl60493.00.0500000.816667v BangladeshChennai13-Oct-2310.00.04.901NaNNaNNaNNaNNaN
4AFGNoor Ahmad (AFG)bowl60493.00.0500000.816667v PakistanChennai23-Oct-2310.00.04.901NaNNaNNaNNaNNaN
5AFGMujeeb Ur Rahman (AFG)bowl60513.00.0500000.850000v EnglandDelhi15-Oct-2310.01.05.102NaNNaNNaNNaNNaN
6ENGAU Rashid (ENG)bowl48543.00.0625001.125000v NetherlandsPune8-Nov-238.00.06.752NaNNaNNaNNaNNaN
7NEDLV van Beek (NED)bowl53603.00.0566041.132075v South AfricaDharamsala17-Oct-238.50.06.792NaNNaNNaNNaNNaN
8BANMehidy Hasan Miraz (BAN)bowl54603.00.0555561.111111v PakistanEden Gardens31-Oct-239.00.06.662NaNNaNNaNNaNNaN
9PAKMohammad Wasim (1) (PAK)bowl60603.00.0500001.000000v New ZealandBengaluru4-Nov-2310.00.06.001NaNNaNNaNNaNNaN
teamplayerbat_or_bowlbb_bfrunswktswicketball_probruns_per_balloppositiongroundstart_dateoversmdnseconinns4s6ssrnot_outmins
1398INDMohammed Siraj (IND)bowl54781.00.0185191.444444v New ZealandWankhede15-Nov-239.00.08.662NaNNaNNaNNaNNaN
1399NZTA Boult (NZ)bowl60861.00.0166671.433333v IndiaWankhede15-Nov-2310.00.08.601NaNNaNNaNNaNNaN
1400NZGD Phillips (NZ)bowl30330.00.0000001.100000v IndiaWankhede15-Nov-235.00.06.601NaNNaNNaNNaNNaN
1401AUSGJ Maxwell (AUS)bowl60350.00.0000000.583333v South AfricaEden Gardens16-Nov-2310.00.03.501NaNNaNNaNNaNNaN
1402SAM Jansen (SA)bowl26350.00.0000001.346154v AustraliaEden Gardens16-Nov-234.20.08.072NaNNaNNaNNaNNaN
1403NZMJ Santner (NZ)bowl60510.00.0000000.850000v IndiaWankhede15-Nov-2310.01.05.101NaNNaNNaNNaNNaN
1404AUSA Zampa (AUS)bowl42550.00.0000001.309524v South AfricaEden Gardens16-Nov-237.00.07.851NaNNaNNaNNaNNaN
1405NZR Ravindra (NZ)bowl42600.00.0000001.428571v IndiaWankhede15-Nov-237.00.08.571NaNNaNNaNNaNNaN
1406INDRA Jadeja (IND)bowl60630.00.0000001.050000v New ZealandWankhede15-Nov-2310.00.06.302NaNNaNNaNNaNNaN
1407NZLH Ferguson (NZ)bowl48650.00.0000001.354167v IndiaWankhede15-Nov-238.00.08.121NaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

teamplayerbat_or_bowlbb_bfrunswktswicketball_probruns_per_balloppositiongroundstart_dateoversmdnseconinns4s6ssrnot_outmins# duplicates
0ENGAAP Atkinson (ENG)bat10NaN1.00.0v PakistanEden Gardens11-Nov-23NaNNaNNaN10.00.00.00.01.02
1PAKAbdullah Shafique (PAK)bat20NaN0.50.0v EnglandEden Gardens11-Nov-23NaNNaNNaN20.00.00.00.02.02